1. Field of the Invention
The present invention relates to adaptive antennas and more particularly to adaptive antennas employing steepest descent algorithms.
2. Description of the Related Art
Antennas are used to receive signals for imaging, communications and other purposes. Conventional antennas are susceptible to degradation in signal to noise ratio (SNR) performance due to undesired "noise" which intrudes via antenna sidelobes and mainlobes. The noise may comprise deliberate electronic countermeasures, friendly radio frequency interference, clutter scatter returns or natural noise sources. Adaptive antennas typically steer nulls onto undesired sources of interference, thereby enhancing SNR and improving the detection of desired signals.
A typical adaptive antenna using a steepest descent algorithm, such as the Applebaum or least mean square error (LMS) algorithms, includes a plurality of antenna elements. In practice, each antenna element receives signals from the signal environment. The received signals may include undesired interfering signals which degrade SNR. The received antenna element signals are weighted, and the weighted antenna element signals are summed to provide an antenna output signal. The adaptation process involves changing the weights applied to the various antenna element signals such that they substantially converge to an optimal set of weights whereupon interfering signals add substantially destructively and the desired signals add substantially coherently. Thus, ideally, nulls will be formed in the directions of undesired interfering signals.
For example, one particular type of adaptive antenna divides signals received by each antenna element into first signals to be processed and second signals to be left substantially unprocessed. Processing of the first signals ordinarily includes weighting of each of the first signals and summing the weighted signals to provide an antenna output signal. An error signal is generated based upon the difference between the antenna output signal and a desired signal. The desired signal is a signal having a selected center frequency and bandwidth in a particular time interval. For each antenna element, the corresponding substantially unprocessed second signal and the error signal are provided to a respective correlator which provides a respective correlation signal which is inputted to a computer. The computer uses information regarding the correlation of the respective second signals and the error signal in executing a steepest descent algorithm which adjusts the weights to be applied to the first signals.
While adaptive antennas employing steepest descent algorithms generally have been successful, there have been shortcomings with their use. For example, B. Widrow, et al in "Adaptive Antenna Systems", Proceedings of the IEEE, Volume 55, No. 12, December 1967, pages 2143-2159, demonstrated that, ordinarily, for fixed signal environment power levels, there was an upper bound on the power level for which an adaptive antenna using an LMS algorithm would efficiently cause the weights to converge to an optimum set of values.
Furthermore, adaptive antennas often are implemented in a digital system in which the weights applied to the first signals are quantized. The quantization of the weights involves allowing the weights to take on only a discreet set of values. Some advantages of digital adaptive antennas are programmability, temperature insensitivity, and ease of implementation. Unfortunately, quantization of the weights can decrease the efficiency of a steepest descent algorithm executed by the adaptive antenna by inducing a lower bound on the signal environment power level for which the algorithm will efficiently cause the weights to substantially converge to an optimum set of values.
Thus, there has been a need for a digitally implemented adaptive antenna employing a steepest descent algorithm in which the weights determined by the algorithm can be quantized without a resultant reduction in the range of signal environment power levels for which the algorithm efficiently substantially adjusts the weights to an optimum set of values. The present invention meets this need.